Jun 10, 2021 · Deep learning-based approaches to object detection use convolutional neural networks architectures such as RetinaNET, YOLO, CenterNet, SSD, and Region Proposals. Object detection finds applications in fields like self-driving cars, asset inspection, pedestrian detection, or video surveillance.

Significant advances in object detection have been achieved through improved object representation and the use of deep neural network models. This paper examines more closely how object detection has evolved in the era of deep learning over the past years.

Jan 1, 2023 · Object detection is the most crucial and challenging task of computer vision. It has numerous applications in the field of security, military, transportation and medical sciences. In this review, object detection and its different aspects have been covered in detail.

Apr 28, 2025 · This systematic review deconstructs object detection research's evolution, methodology, and challenges by integrating evidence from high-impact repositories. Publication trends, dataset usage, and domination of leading venues like CVPR and ICCV in driving the field are addressed. Comparative performance assessment of YOLO, Faster R-CNN, and DETR considers their performance, scalability, and ...

Leveraging DL allows for the direct learning of feature representations from image data, resulting in advanced performance. This review provides a comprehensive examination of fundamental models and algorithms, with a particular focus on neural network (NN) frameworks utilized for feature extraction.

Sep 29, 2023 · Object detection is a profound computer vision technique that identifies and labels objects within images, videos, and even live footage. Models that perform object detection are trained with a surplus of annotated visuals in order to carry out this process with new data.

Apr 24, 2021 · This article surveys recent developments in deep learning based object detectors. Concise overview of benchmark datasets and evaluation metrics used in detection is also provided along with some of the prominent backbone architectures used in recognition tasks. It also covers contemporary lightweight classification models used on edge devices.

Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles which combine multiple low-level image features with high-level context from …

Apr 29, 2025 · Abstract. This paper presents a groundbreaking and comprehensive review, the first of its kind, focused on 3D object detection with Vision-Language Models (VLMs), a rapidly advancing frontier in multimodal AI.Using a hybrid search strategy combining academic databases and AI-powered engines, we curated and analyzed over 100 state-of-the-art papers.

Apr 28, 2025 · The fusion of language and vision in large vision-language models (LVLMs) has revolutionized deep learning-based object detection by enhancing adaptability, contextual reasoning, and generalization beyond traditional architectures. ... demonstrating the progress made in object detection using Vision-Language Models (VLMs) that facilitate more ...